Finding anomaly detection by pattern matching in a set of continous data
Without knowing the kind of data and the process generating it it's hard to give a definite answer.
In general, I would attempt a network that has as inputs the actual sensor readings, and outputs the expected readings. You train this network by presenting data with errors added as inputs, and correct readings as outputs.  It should learn to guess the correct (non-anomalous) readings from a set of actual readings, and you can find the sensors with anomalies by taking the difference between actual and predicted readings.
Depending on the kind of anomalies you expect (misreadings such as fluke zero or max values, or actually anomalous states of the system being measured) your training data should be set up to have samples of such anomalies.
If there are temporal correlations (for example, temperature readings that change slowly) using a RNN might be helpful but its dimensioning heavily depends on the nature of your measured system.
External factors influencing the system (state of a heating mechanism, time of day/year, etc.) could be added at inputs for better predictions.
At the end of the day, trial and error is your friend. Start with a simple network and see how well it behaves. Go for more complex networks when you see where the limitations of the simple solution are.